mirror of
https://github.com/K-Dense-AI/claude-scientific-skills.git
synced 2026-03-27 07:09:27 +08:00
Added parallel-web skill
Refactor research lookup skill to enhance backend routing and update documentation. The skill now intelligently selects between the Parallel Chat API and Perplexity sonar-pro-search based on query type. Added compatibility notes, license information, and improved descriptions for clarity. Removed outdated example scripts to streamline the codebase.
This commit is contained in:
Vinayak Agarwal
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scientific-skills/parallel-web/references/api_reference.md
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scientific-skills/parallel-web/references/api_reference.md
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# Parallel Web Systems API Quick Reference
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**Full Documentation:** https://docs.parallel.ai
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**API Key:** https://platform.parallel.ai
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**Python SDK:** `pip install parallel-web`
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**Environment Variable:** `PARALLEL_API_KEY`
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---
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## Search API (Beta)
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**Endpoint:** `POST https://api.parallel.ai/v1beta/search`
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**Header:** `parallel-beta: search-extract-2025-10-10`
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### Request
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```json
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{
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"objective": "Natural language search goal (max 5000 chars)",
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"search_queries": ["keyword query 1", "keyword query 2"],
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"max_results": 10,
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"excerpts": {
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"max_chars_per_result": 10000,
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"max_chars_total": 50000
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},
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"source_policy": {
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"allow_domains": ["example.com"],
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"deny_domains": ["spam.com"],
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"after_date": "2024-01-01"
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}
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}
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```
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### Response
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```json
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{
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"search_id": "search_...",
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"results": [
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{
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"url": "https://...",
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"title": "Page Title",
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"publish_date": "2025-01-15",
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"excerpts": ["Relevant content..."]
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}
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]
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}
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```
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### Python SDK
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```python
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from parallel import Parallel
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client = Parallel(api_key="...")
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result = client.beta.search(
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objective="...",
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search_queries=["..."],
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max_results=10,
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excerpts={"max_chars_per_result": 10000},
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)
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```
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**Cost:** $5 per 1,000 requests (default 10 results each)
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**Rate Limit:** 600 requests/minute
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---
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## Extract API (Beta)
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**Endpoint:** `POST https://api.parallel.ai/v1beta/extract`
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**Header:** `parallel-beta: search-extract-2025-10-10`
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### Request
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```json
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{
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"urls": ["https://example.com/page"],
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"objective": "What to focus on",
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"excerpts": true,
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"full_content": false
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}
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```
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### Response
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```json
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{
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"extract_id": "extract_...",
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"results": [
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{
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"url": "https://...",
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"title": "Page Title",
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"excerpts": ["Focused content..."],
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"full_content": null
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}
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],
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"errors": []
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}
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```
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### Python SDK
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```python
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result = client.beta.extract(
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urls=["https://..."],
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objective="...",
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excerpts=True,
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full_content=False,
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)
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```
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**Cost:** $1 per 1,000 URLs
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**Rate Limit:** 600 requests/minute
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---
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## Task API (Deep Research)
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**Endpoint:** `POST https://api.parallel.ai/v1/tasks/runs`
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### Create Task Run
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```json
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{
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"input": "Research question (max 15,000 chars)",
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"processor": "pro-fast",
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"task_spec": {
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"output_schema": {
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"type": "text"
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}
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}
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}
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```
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### Response (immediate)
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```json
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{
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"run_id": "trun_...",
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"status": "queued"
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}
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```
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### Get Result (blocking)
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**Endpoint:** `GET https://api.parallel.ai/v1/tasks/runs/{run_id}/result`
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### Python SDK
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```python
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# Text output (markdown report with citations)
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from parallel.types import TaskSpecParam
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task_run = client.task_run.create(
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input="Research question",
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processor="pro-fast",
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task_spec=TaskSpecParam(output_schema={"type": "text"}),
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)
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result = client.task_run.result(task_run.run_id, api_timeout=3600)
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print(result.output.content)
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# Auto-schema output (structured JSON)
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task_run = client.task_run.create(
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input="Research question",
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processor="pro-fast",
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)
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result = client.task_run.result(task_run.run_id, api_timeout=3600)
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print(result.output.content) # structured dict
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print(result.output.basis) # citations per field
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```
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### Processors
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| Processor | Latency | Cost/1000 | Best For |
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|-----------|---------|-----------|----------|
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| `lite-fast` | 10-20s | $5 | Basic metadata |
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| `base-fast` | 15-50s | $10 | Standard enrichments |
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| `core-fast` | 15s-100s | $25 | Cross-referenced |
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| `core2x-fast` | 15s-3min | $50 | High complexity |
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| **`pro-fast`** | **30s-5min** | **$100** | **Default: exploratory research** |
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| `ultra-fast` | 1-10min | $300 | Deep multi-source |
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| `ultra2x-fast` | 1-20min | $600 | Difficult research |
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| `ultra4x-fast` | 1-40min | $1200 | Very difficult |
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| `ultra8x-fast` | 1hr | $2400 | Most difficult |
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Standard (non-fast) processors have the same cost but higher latency and freshest data.
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---
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## Chat API (Beta)
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**Endpoint:** `POST https://api.parallel.ai/chat/completions`
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**Compatible with OpenAI SDK.**
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### Models
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| Model | Latency (TTFT) | Cost/1000 | Use Case |
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|-------|----------------|-----------|----------|
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| `speed` | ~3s | $5 | Low-latency chat |
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| `lite` | 10-60s | $5 | Simple lookups with basis |
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| `base` | 15-100s | $10 | Standard research with basis |
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| `core` | 1-5min | $25 | Complex research with basis |
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### Python SDK (OpenAI-compatible)
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```python
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from openai import OpenAI
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client = OpenAI(
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api_key="PARALLEL_API_KEY",
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base_url="https://api.parallel.ai",
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)
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response = client.chat.completions.create(
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model="speed",
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messages=[{"role": "user", "content": "What is Parallel Web Systems?"}],
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)
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```
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---
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## Rate Limits
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| API | Default Limit |
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|-----|---------------|
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| Search | 600 req/min |
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| Extract | 600 req/min |
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| Chat | 300 req/min |
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| Task | Varies by processor |
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---
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## Source Policy
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Control which sources are used in searches:
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```json
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{
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"source_policy": {
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"allow_domains": ["nature.com", "science.org"],
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"deny_domains": ["unreliable-source.com"],
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"after_date": "2024-01-01"
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}
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}
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```
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Works with Search API and can be used to focus results on specific authoritative domains.
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